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Rapid differentiation of Xihuangcao from the three Isodon species by UPLC-ESI-QTOF-MS/MS and chemometrics analysis

Overview of attention for article published in Chinese Medicine, December 2016
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Title
Rapid differentiation of Xihuangcao from the three Isodon species by UPLC-ESI-QTOF-MS/MS and chemometrics analysis
Published in
Chinese Medicine, December 2016
DOI 10.1186/s13020-016-0120-y
Pubmed ID
Authors

Lai Lai Wong, Zhitao Liang, Hubiao Chen, Zhongzhen Zhao

Abstract

Isodon lophanthoides, I. lophanthoides var. graciliflorus and I. serra are the three botanical sources of Xihuangcao, which are often used indiscriminately in herbal products. The aim of this study was to develop a rapid and accurate analytical method to identify the three different botanical sources of Xihuangcao by combining UPLC-ESI-QTOF-MS with chemometrics analysis. Fifteen batches of plants were collected as reference materials and their chemical profiles were analyzed by UPLC-ESI-QTOF-MS. These data were subsequently processed by statistical methods, including principal component analysis (PCA), hierarchical cluster analysis (HCA) and orthogonal partial least squared discriminant analysis (OPLS-DA). An automated sample class prediction model was also built using Naive Bayes as a class prediction algorithm to rapidly determine the source species of twenty-seven batches of commercial Xihuangcao samples. The base peak chromatograms of the three authenticated species showed different patterns and twenty-seven peaks were chosen, including six diterpenoids, one phenolic acid and two glycosides to distinguish among these three species. The results showed good differentiation among the three species by PCA, HCA and OPLS-DA. Isodon lophanthoides var. graciliflorus was found to be the major botanical source of the commercial samples. UPLC-ESI-QTOF-MS and subsequent chemometrics analysis were demonstrated effective to differentiate among the three different species of plants used as Xihuangcao.

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Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 22%
Other 1 11%
Professor 1 11%
Student > Master 1 11%
Professor > Associate Professor 1 11%
Other 1 11%
Unknown 2 22%
Readers by discipline Count As %
Chemistry 2 22%
Medicine and Dentistry 2 22%
Biochemistry, Genetics and Molecular Biology 1 11%
Environmental Science 1 11%
Agricultural and Biological Sciences 1 11%
Other 0 0%
Unknown 2 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 December 2016.
All research outputs
#16,046,765
of 25,371,288 outputs
Outputs from Chinese Medicine
#256
of 660 outputs
Outputs of similar age
#241,819
of 421,129 outputs
Outputs of similar age from Chinese Medicine
#4
of 8 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 660 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has gotten more attention than average, scoring higher than 58% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 421,129 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.